A growing appreciation of the importance of cellular metabolism and revelations concerning the extent of cell–cell heterogeneity demand metabolic characterization of individual cells. We present SpaceM, an open-source method for in situ single-cell metabolomics that detects >100 metabolites from >1,000 individual cells per hour, together with a fluorescence-based readout and retention of morpho-spatial features. We validated SpaceM by predicting the cell types of cocultured human epithelial cells and mouse fibroblasts. We used SpaceM to show that stimulating human hepatocytes with fatty acids leads to the emergence of two coexisting subpopulations outlined by distinct cellular metabolic states. Inducing inflammation with the cytokine interleukin-17A perturbs the balance of these states in a process dependent on NF-κB signaling. The metabolic state markers were reproduced in a murine model of nonalcoholic steatohepatitis. We anticipate SpaceM to be broadly applicable for investigations of diverse cellular models and to democratize single-cell metabolomics.
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All MALDI-imaging MS data as well as metabolite and lipid annotations and images are publicly available through METASPACE (https://metaspace2020.eu/project/Rappez_2021_SpaceM). The MALDI-imaging MS data and LC–MS/MS datasets are available in the MetaboLights repository under accession number MTBLS78. The microscopy data are available at the European Bioinformatics Institute BioStudies repository under accession number S-BSST369. Source data are provided with this paper.
The SpaceM codebase is accessible as Supplementary Software and on GitHub (https://github.com/alexandrovteam/SpaceM). The spatio-molecular matrices and the code for their downstream processing, including generation of the main figures, are available on Google Collaboratory (https://colab.research.google.com/drive/1CKdHDUkGIpAcBzrSfuCodMF_l2xbVAKT).
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We thank C.B. Vibe for her support and feedback on the manuscript, M. Shahraz, M. Ekelhof and A. Palmer for help with MALDI-imaging, A. Eisenbarth for software development, N. Typas for advising on biology and providing access to a microscope, B. El Debs and J. Selkrig for training on microscopy and cell culturing, METASPACE software development team (all EMBL), M. Stanifer and S. Boulant (DKFZ) for training on cell culturing, A. Andersen (Life Science Editors) and T. O’Connor (Helmholtz Center Munich) for scientific editing, S. Seah and C. Merten (EMBL) for providing NIH3T3-GFP, F. Merkel and C. Häring (EMBL) for providing HeLa Kyoto H2B-mCherry. We thank other members of the Thesis Advisory Committee of L.R., A.-C. Gavin (EMBL) and B. Brügger (Heidelberg University). This work was funded in part by the European Union’s Horizon 2020 program under agreement numbers 634402, 777222 (T.A.) and 667273 (M.H.), the DKFZ-MOST cooperation program (M.H., M.S.), Darwin Trust of Edinburgh (S.T.), SFB Transregio grant nos. 179, 209, 1335 and I&I Helmholtz Zukunftsthema (all M.H.), the ERC Consolidator grants HepatoMetaboPath (M.H.) and METACELL (T.A.) and ERC Proof of Concept Faith (M.H.). We thank all the reviewers and the editor for detailed feedback that helped improve the paper.
L.R. and T.A. are the inventors on a patent application describing a spatial single-cell metabolomics method (application in the EU EP3610267A1, US US20200057049A1, Canada CA3059818A1, Australia AU2018252185A1, World Intellectual Property Organization (Patent Cooperation Treaty) WO2018189365A1).
Peer review information Nature Methods thanks Young Jin Lee, Peter Nemes and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Rita Strack was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended Data Fig. 1 Procedure of detection of laser ablation marks in post-MALDI microscopy images.
The post-MALDI microscopy image is manually cropped around the baltion marks. The Fourier Transform is computed, followed by a low pass filter. The inverse Fourier Transform generates a denoised image with features having spatial frequencies associated with the ablation marks becoming more pronounced. This enables a robust histogram-based thresholding to segment individual ablation marks and determine their centroid coordinates. Every centroid is used as a seed for a region growing algorithm to determine the edges of each ablation mark.
Pen marks are drawn with a black sharpie on the glass slide at the edges of the culturing area. These black pen marks are visible in both the pre- and post-MALDI microscopy images. Histogram-based thresholding is applied to both microscopy images to extract the penmarks areas followed by an edge detection that detects pixels on the edge of the pen marks. This generates more than 400.000 individual features for each microscopy image. A random subset of 5000 features from both pre- (in blue) and post-MALDI (in red) images are used as fiducials to estimate the coordinate transformation for image registration. The overlap of both pre- and post-MALDI fiducials is illustrated. The estimated coordinate transformation from post- to pre-MALDI is applied to the ablation mark coordinates (shown in green) in order to estimate their position in the pre-MALDI microscopy image.
The indexation of the segmented ablation marks involved fitting a theoretical rectangular grid to the ablation marks. In A, the grid angle is estimated by minimizing the number of non-overlapping ablation mark coordinates after projection of the X axis for different rotation angles. In B, the center of the grid is estimated from the extrema of the ablation mark coordinates. The spacing of the grid nodes is estimated in C by minimizing the mean distance for each grid note to the nearest ablation mark. The re-indexing in D is done by choosing the closest ablation mark coordinates from the grid nodes constructed using the parameters defined before (the grid nodes are shown in red, their nearest ablation mark coordinates are shown in black). In E, the ablation mark coordinates are color-coded by their index. An illustration of the different steps for fitting a grid onto the ablation mark coordinates as well as the re-indexing is shown in F. In G, the re-indexed ablation marks are shown.
The SpaceM processing is composed of two parts: filtering ablation marks and normalizing metabolite intensities across single cells. First, for each cell, its touching ablation marks are filtered based on their area, their sampling proportions (proportion of the ablation mark area sampling any cell) and their sampling specificity (the proportion of their sampling area shared with the cell of interest). Ablation marks sampling predominantly extracellular areas or too many cells at the same time are filtered out (as illustrated here for the ablation mark II and III). Second, for a given metabolite, its intensity in a cell is calculated as a weighted mean of the metabolite intensities from the filtered ablation marks sampling that cell. The intensities are divided by the sampling proportion to account for differences in amount of sampled cellular material between ablation marks. To increase the contribution of ablation marks which sample the cell of interest more than other ablation marks, their intensities are weighted by the sampling specificity. ‘area(a)’ stands for the area of an ablation mark a; ‘sampling area(a)’ stands for the intracellular area of ablation mark ‘a’; ‘area(c)’ stands for the area of a cell ‘c’; all areas are computed in microscopy pixels.
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Rappez, L., Stadler, M., Triana, S. et al. SpaceM reveals metabolic states of single cells. Nat Methods 18, 799–805 (2021). https://doi.org/10.1038/s41592-021-01198-0